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Sensors 2015, 15(10), 26940-26960; doi:10.3390/s151026940

AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal

1
School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China
2
Inertial Technology Key Laboratory of National Defense Science and Technology, Beihang University, Beijing 100191, China
3
Beijing Institute of Spacecraft System Engineering, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 20 August 2015 / Revised: 28 September 2015 / Accepted: 10 October 2015 / Published: 23 October 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [761 KB, uploaded 26 October 2015]   |  

Abstract

An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal. View Full-Text
Keywords: Adaptive Moving Average (AMA); Random Weighting Estimation (RWE); Fiber Optic Gyroscope (FOG); Kalman Filter (KF) Adaptive Moving Average (AMA); Random Weighting Estimation (RWE); Fiber Optic Gyroscope (FOG); Kalman Filter (KF)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yang, G.; Liu, Y.; Li, M.; Song, S. AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal. Sensors 2015, 15, 26940-26960.

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